I’ve been on a major QBkick lately, and there’s no reason to stop now. Today, I want to look at a method that might tease out a quarterback’s “true talent” better than if we simply use his raw stats from the season.

Three years ago, our colleague Jason Lisk had a post on the old PFR Blog about which rate stats stay consistent when a QB changes teams. Basically, he grabbed QBs who were still in their primes and changed teams, looking at how their key rate stats correlated from one year to the next. Here’s what Jason found:

[…]I looked at the correlation coefficient for our group of 48 passers, for the year N advanced passing score compared to the year N+1 advanced passing score in each category. This should tell us whether the passers who were good in a performance area (or bad) tended to be the ones who remained good in that performance area the following season, even with the uncertainty of team changes (some positive, some negative for the quarterback).

What do those correlations mean, exactly? Well, take sack percentage as an example. In general, a correlation of 0.31 means you can expect 31% of a QB’s difference from the mean to be repeated next year when he changes teams. In other words, you have to regress the QB’s sack rate 69% towards the mean to get the true rate that “belongs” to him. If the average sack rate is 6.1%, and a QB has a rate of 4.0% (like, say, Drew Brees this year), his “true” sack rate is probably something like 5.4% — 31% of the distance between .061 and .040.

The same concept applies to the other stats listed above. Tony Romo’s observed 66.7% completion percentage is really more like 62.5% after regressing to the mean, and so forth. Do that for every QB who had a reasonable number of attempts this year, and you get these rate stats:

Now we just need to reconstruct the player’s raw passing line as though he posted those rate stats instead of his actual rates. Cmp%, YPA, TD%, and INT% are easy (just multiply by attempts), and Sack% can be derived via simple algebra:

Sacks_new = (-reg_sk% * Attempts) / (reg_sk% – 1)

(Sack yards can be assumed by multiplying raw sack yards per sack by the new sack total.)

Finally, we plug the new totals into the Adjusted Net Yards Per Attempt formula, and we have a QB stat that is sort of like baseball’s Fielding Independent Pitching (FIP), which also seeks to reduce the noise and teammate interactions in a pitcher’s ERA by reducing his performance to only those elements he has control over — strikeouts, walks, and home runs.

Here are the 2012 leaders in QB-FIP (along with their regressed totals):

If you have multiple years of data, then that would be helpful to include–true talent level works best with multiple years of data, particularly for stats as unstable as these. (FIP does not regress to the mean at all, but does use the few stats that would have very high YtY correlations, of which there seem to be none for QBs).

Red

What would the correlations look like if you included all QB’s in your sample, not just the ones who changed teams? That would paint a more realistic picture of what to expect for any given QB in “Year+1”.

When you look at a QB like Peyton Manning, his regressed numbers seem way too regressed. The above chart seems to imply that Manning is only worth 0.3 ANYA by himself, and that his teammates/luck are worth 1.4 ANYA (his unadjusted ANYA is 1.7 above average). Put another way, the chart implies that Manning only contributes 18% of the marginal value of the Broncos’ passing attack (0.3/1.7), and that just seems way off. His ANYA has been well above average every season since his rookie year, despite playing with an array of different teammates. To me, that would indicate that Manning himself is mostly resposible for the success of his teams’ passing games, and that his “true” talent level is somewhere between 7.0 and 7.5 ANYA.